arXiv subjects

Samuel Cheng

Publications and source records attributed to Samuel Cheng.

18 recordsLinked to original sources

arXiv2025

Water Mapping and Change Detection Using Time Series Derived from the Continuous Monitoring of Land Disturbance Algorithm

Given the growing environmental challenges, accurate monitoring and prediction of changes in water bodies are essential for sustainable management and conservation. The Continuous Monitoring of Land Disturbance (COLD) algorithm provides a valuable tool for real-time analysis of land changes, such as deforestation, urban expansion, agricultural activities, and natural disasters. This capability enables timely interventions and more informed decision-making. This paper assesses the effectiveness of the algorithm to estimate water bodies and track pixel-level water trends over time. Our findings indicate that COLD-derived data can reliably estimate estimate water frequency during stable periods and delineate water bodies. Furthermore, it enables the evaluation of trends in water areas after disturbances, allowing for the determination of whether water frequency increases, decreases, or remains constant.

cs.LG↗

arXiv2024

Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models

Despite the advances in medicine, cancer has remained a formidable challenge. Particularly in the case of pancreatic tumors, characterized by their diversity and late diagnosis, early detection poses a significant challenge crucial for effective treatment. The advancement of deep learning techniques, particularly supervised algorithms, has significantly propelled pancreatic tumor detection in the medical field. However, supervised deep learning approaches necessitate extensive labeled medical images for training, yet acquiring such annotations is both limited and costly. Conversely, weakly supervised anomaly detection methods, requiring only image-level annotations, have garnered interest. Existing methodologies predominantly hinge on generative adversarial networks (GANs) or autoencoder models, which can pose complexity in training and, these models may face difficulties in accurately preserving fine image details. This research presents a novel approach to pancreatic tumor detection, employing weak supervision anomaly detection through denoising diffusion algorithms. By incorporating a deterministic iterative process of adding and removing noise along with classifier guidance, the method enables seamless translation of images between diseased and healthy subjects, resulting in detailed anomaly maps without requiring complex training protocols and segmentation masks. This study explores denoising diffusion models as a recent advancement over traditional generative models like GANs, contributing to the field of pancreatic tumor detection. Recognizing the low survival rates of pancreatic cancer, this study emphasizes the need for continued research to leverage diffusion models' efficiency in medical segmentation tasks.

eess.IV↗

arXiv2022

Bayesian Federated Neural Matching that Completes Full Information

Federated learning is a contemporary machine learning paradigm where locally trained models are distilled into a global model. Due to the intrinsic permutation invariance of neural networks, Probabilistic Federated Neural Matching (PFNM) employs a Bayesian nonparametric framework in the generation process of local neurons, and then creates a linear sum assignment formulation in each alternative optimization iteration. But according to our theoretical analysis, the optimization iteration in PFNM omits global information from existing. In this study, we propose a novel approach that overcomes this flaw by introducing a Kullback-Leibler divergence penalty at each iteration. The effectiveness of our approach is demonstrated by experiments on both image classification and semantic segmentation tasks.

cs.LG↗

arXiv2022

The Star-Planet Activity Research CubeSat (SPARCS): Determining Inputs to Planetary Habitability

Seventy-five billion low-mass stars in our galaxy host at least one small planet in their habitable zone (HZ). The stellar ultraviolet (UV) radiation received by the planets is strong and highly variable, and has consequences for atmospheric loss, composition, and habitability. SPARCS is a NASA-funded mission to characterize the quiescent and flare UV emission from low-mass stars, by observing 10 to 20 low-mass stars, over timescales of days, simultaneously in two UV bands: 153-171 nm and 260-300 nm. SPARCS Sun-synchronous terminator orbit allows for long periods of uninterrupted observations, reaching 10s of days for some targets. The payload consists of a 10 cm-class telescope, a dichroic element, UV detectors and associated electronics, a thermal control system, and an on-board processor. The payload is hosted on a Blue Canyon Technologies 6U CubeSat. SPARCS hosts several technology innovations that have broad applicability to other missions. The payload demonstrates the use of "2D-doped" (i.e., delta- and superlattice-doped) detectors and detector-integrated metal dielectric filters in space. This detector technology provides ~5x larger quantum efficiency than NASA's GALEX detectors. In addition, SPARCS' payload processor provides dynamic exposure control, automatically adjusting the exposure time to avoid flare saturation and to time-resolve the strongest stellar flares. A simple passive cooling system maintains the detector temperature under 238K to minimize dark current. The spacecraft bus provides pointing jitter smaller than 6", minimizing the impact of flat-field errors, dark current, and read-noise. All these elements enable competitive astrophysics science within a CubeSat platform. SPARCS is currently in the final design and fabrication phase (Phase C in the NASA context). It will be launched in 2024, for a primary science mission of one year.

astro-ph.IM↗

arXiv2021

Onboard Dynamic Image Exposure Control for the Star-Planet Activity Research CubeSat (SPARCS)

The Star-Planet Activity Research CubeSat (SPARCS) is a 6U CubeSat under development to monitor the flaring and chromospheric activity of M dwarfs at near-ultraviolet (NUV) and far-ultraviolet (FUV) wavelengths. The spacecraft hosts two UV-optimized delta-doped charge-coupled devices fed by a 9-cm telescope and a dichroic beam splitter. A dedicated science payload processor performs near real-time onboard science image processing to dynamically change detector integration times and gains to reduce the occurrence of pixel saturation during strong M dwarf flaring events and provide adequate flare light curve structure resolution while enabling the detection of low-amplitude rotational modulation. The processor independently controls the NUV and FUV detectors. For each detector, it derives control updates from the most recent completed exposure and applies them to the next exposure. The detection of a flare event in the NUV channel resets the exposure in the FUV channel with new exposure parameters. Implementation testing of the control algorithm using simulated light curves and full-frame images demonstrates a robust response to the quiescent and flaring levels expected for the stars to be monitored by the mission. The SPARCS onboard autonomous exposure control algorithm is adaptable for operation in future point source-targeting space-based and ground-based observatories geared towards the monitoring of extreme transient astrophysics phenomena.

astro-ph.IM↗

arXiv2021

Time-Resolved Photometry of the High-Energy Radiation of M Dwarfs with the Star-Planet Activity Research CubeSat (SPARCS)

Know thy star, know thy planet,... especially in the ultraviolet (UV). Over the past decade, that motto has grown from mere wish to necessity in the M dwarf regime, given that the intense and highly variable UV radiation from these stars is suspected of strongly impacting their planets' habitability and atmospheric loss. This has led to the development of the Star-Planet Activity Research CubeSat (SPARCS), a NASA-funded 6U CubeSat observatory fully devoted to the photometric monitoring of the UV flaring of M dwarfs hosting potentially habitable planets. The SPARCS science imaging system uses a 9-cm telescope that feeds two delta-doped UV-optimized CCDs through a dichroic beam splitter, enabling simultaneous monitoring of a target field in the near-UV and far-UV. A dedicated onboard payload processor manages science observations and performs near-real time image processing to sustain an autonomous dynamic exposure control algorithm needed to mitigate pixel saturation during flaring events. The mission is currently half-way into its development phase. We present an overview of the mission's science drivers and its expected contribution to our understanding of star-planet interactions. We also present the expected performance of the autonomous dynamic exposure control algorithm, a first-of-its-kind on board a space-based stellar astrophysics observatory.

astro-ph.IM↗

arXiv2021

Capsule networks with non-iterative cluster routing

Capsule networks use routing algorithms to flow information between consecutive layers. In the existing routing procedures, capsules produce predictions (termed votes) for capsules of the next layer. In a nutshell, the next-layer capsule's input is a weighted sum over all the votes it receives. In this paper, we propose non-iterative cluster routing for capsule networks. In the proposed cluster routing, capsules produce vote clusters instead of individual votes for next-layer capsules, and each vote cluster sends its centroid to a next-layer capsule. Generally speaking, the next-layer capsule's input is a weighted sum over the centroid of each vote cluster it receives. The centroid that comes from a cluster with a smaller variance is assigned a larger weight in the weighted sum process. Compared with the state-of-the-art capsule networks, the proposed capsule networks achieve the best accuracy on the Fashion-MNIST and SVHN datasets with fewer parameters, and achieve the best accuracy on the smallNORB and CIFAR-10 datasets with a moderate number of parameters. The proposed capsule networks also produce capsules with disentangled representation and generalize well to images captured at novel viewpoints. The proposed capsule networks also preserve 2D spatial information of an input image in the capsule channels: if the capsule channels are rotated, the object reconstructed from these channels will be rotated by the same transformation. Codes are available at https://github.com/ZHAOZHIHAO/ClusterRouting.

cs.CV↗

arXiv2021

How likely is a random graph shift-enabled?

The shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random graph is shift-enabled. In particular, popular graph models ER, WS, BA random graph are used, weighted and unweighted, as well as signed graphs. Our results show that the considered unweighted connected random graphs are shift-enabled with high probability when the number of edges is moderately high. However, very dense graphs, as well as fully connected graphs, are not shift-enabled. Interestingly, this behaviour is not observed for weighted connected graphs, which are always shift-enabled unless the number of edges in the graph is very low.

cs.DM↗

arXiv2020

Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information

In federated learning, models trained on local clients are distilled into a global model. Due to the permutation invariance arises in neural networks, it is necessary to match the hidden neurons first when executing federated learning with neural networks. Through the Bayesian nonparametric framework, Probabilistic Federated Neural Matching (PFNM) matches and fuses local neural networks so as to adapt to varying global model size and the heterogeneity of the data. In this paper, we propose a new method which extends the PFNM with a Kullback-Leibler (KL) divergence over neural components product, in order to make inference exploiting posterior information in both local and global levels. We also show theoretically that The additional part can be seamlessly concatenated into the match-and-fuse progress. Through a series of simulations, it indicates that our new method outperforms popular state-of-the-art federated learning methods in both single communication round and additional communication rounds situation.

cs.LG↗

arXiv2020

Delta-doped Electron Multiplying CCDs for FIREBall-2

We present the status of on-going detector development efforts for our joint NASA/CNES balloon-borne UV multi-object spectrograph, the Faint Intergalactic Redshifted Emission Balloon (FIREBall-2; FB-2). FB-2 demonstrates a new UV detector technology, the delta-doped Electron Multiplying CCD (EMCCD), in a low risk suborbital environment, to prove the performance of EMCCDs for future space missions and Technology Readiness Level (TRL) advancement. EMCCDs can be used in photon counting (PC) mode to achieve extremely low readout noise ($<$1 electron). Our testing has focused on reducing clock-induced-charge (CIC) through wave shaping and well depth optimization with a \nuvu V2 CCCP Controller, measuring CIC at 0.001 e$^{-}$/pixel/frame. This optimization also includes methods for reducing dark current, via cooling, and substrate voltage levels. We discuss the challenges of removing cosmic rays, which are also amplified by these detectors, as well as a data reduction pipeline designed for our noise measurement objectives. FB-2 flew in 2018, providing the first time an EMCCD was used for UV observations in the stratosphere. FB-2 is currently being built up to fly again in 2020, and improvements are being made to the EMCCD to continue optimizing its performance for better noise control.

astro-ph.IM↗

arXiv2019

Implementing distributed graph filters by elementary matrix decomposition

In this letter, we consider the implementation problem of distributed graph filters, where each node only has access to the signals of the current and its neighboring nodes. By using Gaussian elimination, we show that as long as the graph is connected, we can implement any graph filter by decomposing the filter into a product of directly implementable filters, filters that only use the signals at the current and neighboring nodes as inputs. We have also included a concrete example as an illustration.

eess.SP↗

arXiv2019

Neural Network for NILM Based on Operational State Change Classification

Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy disaggregation problem. However, most proposed methods try to estimate the on-off state or the power consumption of appliance, which need not only large amount of parameters, but also hyper-parameter optimization prior to training and even preprocessing of energy data for a specified appliance. In this paper, instead of estimating on-off state or power consumption, we adapt a neural network to estimate the operational state change of appliance. Our proposed solution is more feasible across various appliances and lower complexity comparing to previous methods. The simulated experiments in the low sample rate dataset REDD show the competitive performance of the designed method, with respect to other two benchmark methods, Hidden Markov Model-based and Graph Signal processing-based approaches.

cs.LG↗

arXiv2018

Undirected graphs: is the shift-enabled condition trivial or necessary?

It has recently been shown that, contrary to the wide belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-symmetric shift matrix. This letter extends this prior work, focusing on undirected graphs where the shift matrix is generally symmetric. We show that while, in this case, the shift matrix can be converted to satisfy the original shift-enabled condition, the converted matrix is not associated with the original graph, that is, it does not capture anymore the structure of the graph signal. We show via a counterexample, that a non-shift-enabled matrix cannot be converted to a shift-enabled one and still maintain the topological structure of the underlying graph, which is necessary to facilitate localized signal processing.

eess.SP↗

arXiv2017

Shift-enabled graphs: Graphs where shift-invariant filters are representable as polynomials of shift operations

In digital signal processing, shift-invariant filters can be represented as a polynomial expansion of a shift operation,that is, the Z-transform representation. When extended to graph signal processing (GSP), this would mean that a shift-invariant graph filter can be represented as a polynomial of the adjacency (shift) matrix of the graph. However, the characteristic and minimum polynomials of the adjacency matrix must be identical for the property to hold. While it has been suggested that this condition might be ignored as it is always possible to find a polynomial transform to represent the original adjacency matrix by another adjacency matrix that satisfies the condition, this letter shows that a filter that is shift invariant in terms of the original graph may not be shift invariant anymore under the modified graph and vice versa. We introduce the notion of "shift-enabled graph" for graphs that satisfy the aforementioned condition, and present a concrete example of a graph that is not "shift-enabled" and a shift-invariant filter that is not a polynomial of the shift operation matrix. The result provides a deeper understanding of shift-invariant filters when applied in GSP and shows that further investigation of shift-enabled graphs is needed to make it applicable to practical scenarios.

eess.SP↗

arXiv2015

Binary Rate Distortion With Side Information: The Asymmetric Correlation Channel Case

Advancing over up-to-date information theoretic results that assume symmetric correlation models, in this work we consider the problem of lossy binary source coding with side information, where the correlation is expressed by a generic binary asymmetric channel. Specifically, we present an in-depth analysis of rate distortion with side information available to both the encoder and decoder (conventional predictive), as well as the Wyner-Ziv problem for this particular setup. Prompted by our recent results for the Z-channel correlation case, we evaluate the rate loss between the Wyner-Ziv and the conventional predictive coding, as a function of the parameters of the binary asymmetric correlation channel.

cs.IT↗

arXiv2013

Zero-error Slepian-Wolf Coding of Confined Correlated Sources with Deviation Symmetry

In this paper, we use linear codes to study zero-error Slepian-Wolf coding of a set of sources with deviation symmetry, where the sources are generalization of the Hamming sources over an arbitrary field. We extend our previous codes, Generalized Hamming Codes for Multiple Sources, to Matrix Partition Codes and use the latter to efficiently compress the target sources. We further show that every perfect or linear-optimal code is a Matrix Partition Code. We also present some conditions when Matrix Partition Codes are perfect and/or linear-optimal. Detail discussions of Matrix Partition Codes on Hamming sources are given at last as examples.

cs.IT↗

arXiv2010

Decomposition Approach for Low-rank Matrix Completion

In this paper, we describe a low-rank matrix completion method based on matrix decomposition. An incomplete matrix is decomposed into submatrices which are filled with a proposed trimming step and then are recombined to form a low-rank completed matrix. The divide-and-conquer approach can significantly reduce computation complexity and storage requirement. Moreover, the proposed decomposition method can be naturally incorporated into any existing matrix completion methods to attain further gain. Unlike most existing approaches, the proposed method is not based on norm minimization nor SVD decomposition. This makes it possible to be applied beyond real domain and can be used in arbitrary fields including finite fields.

math.NA↗

arXiv2010

Hamming Code for Multiple Sources

We consider Slepian-Wolf (SW) coding of multiple sources and extend the packing bound and the notion of perfect code from conventional channel coding to SW coding with more than two sources. We then introduce Hamming Codes for Multiple Sources (HCMSs) as a potential solution of perfect SW coding for arbitrary number of terminals. Moreover, we study the case with three sources in detail. We present the necessary conditions of a perfect SW code and show that there exists infinite number of HCMSs. Moreover, we show that for a perfect SW code with sufficiently long code length, the compression rates of different sources can be trade-off flexibly. Finally, we relax the construction procedure of HCMS and call the resulting code generalized HCMS. We prove that every perfect SW code for Hamming sources is equivalent to a generalized HCMS.

cs.IT↗